This is AldrinEC’s first assignment for Geog458. Our course page can be accessed here.

This is my favorite video game right now.

This is my favorite video game right now.

The formula to calculate the area of a circle is: \(\pi r^2\)

Course Name Quarter Completed
CSE 142 Wi16
MATH 124 Sp16
GEOG 360 Au18
GEOG 315 Au18

Source: Aldrin Carbonell

Rudimentary R Practice

100/10+2
## [1] 12
100/(10+2)
## [1] 8.333333
80/(15+5)*2-30/6
## [1] 3
45+5*2-10
## [1] 45
200-50*2+(5*3)-20
## [1] 95
x=8*3
x+10
## [1] 34
y=6
z=11
x*y
## [1] 144
(z+10)/3
## [1] 7
(y+z)*38
## [1] 646
nums = seq(1, 30)
strings = c("astronaut","ballerina","camera")
length(nums)
## [1] 30
length(strings)
## [1] 3
sum(nums)
## [1] 465
num2 = seq(1, 5)
num3 = seq(6, 10)
num2 + num3
## [1]  7  9 11 13 15
prod = num2 * num3

twoseqs = c(num2, num3)
rows = rbind(num2, num3, prod)
rows
##      [,1] [,2] [,3] [,4] [,5]
## num2    1    2    3    4    5
## num3    6    7    8    9   10
## prod    6   14   24   36   50
numsdata = as.data.frame(rows)
numsdata
##      V1 V2 V3 V4 V5
## num2  1  2  3  4  5
## num3  6  7  8  9 10
## prod  6 14 24 36 50

Loading the Data

this is how to load data into R and how to convert it.

library(tidyverse)
## -- Attaching packages ---------------------------------------------------------------------------------------------------- tidyverse 1.2.1 --
## v ggplot2 3.1.0     v purrr   0.2.5
## v tibble  1.4.2     v dplyr   0.7.8
## v tidyr   0.8.2     v stringr 1.3.1
## v readr   1.3.1     v forcats 0.3.0
## -- Conflicts ------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
object1=read.csv("China_EO_49to17.csv")
chinadata=as_tibble(object1)
rearrange = arrange(chinadata, desc(Year))
rearrange
## # A tibble: 69 x 63
##     Year Beijing_Enterpr~ Beijing_Output Tianjin_Enterpr~ Tianjin_Output
##    <int>            <int>          <dbl>            <int>          <dbl>
##  1  2017             3231            NA              4286            NA 
##  2  2016             3340         18087.             5203         27402.
##  3  2015             3548         17450.             5525         28017.
##  4  2014             3686         18453.             5501         28079.
##  5  2013             3641         17371.             5511         26400.
##  6  2012             3692         15596.             5342         23428.
##  7  2011             3746         14514.             5013         20863.
##  8  2010             6884         13700.             7947         16752.
##  9  2009             6890         11039.             8326         13084.
## 10  2008             7205         10413.             7950         12503.
## # ... with 59 more rows, and 58 more variables: Hebei_Enterprise <int>,
## #   Hebei_Output <dbl>, Shanxi_Enterprise <int>, Shanxi_Output <dbl>,
## #   InnerMongolia_Enterprise <int>, InnerMongolia_Output <dbl>,
## #   Liaoning_Enterprise <int>, Liaoning_Output <dbl>,
## #   Jilin_Enterprise <int>, Jilin_Output <dbl>,
## #   Heilongjiang_Enterprise <int>, Heilongjiang_Output <dbl>,
## #   Shanghai_Enterprise <int>, Shanghai_Output <dbl>,
## #   Jiangsu_Enterprise <int>, Jiangsu_Output <dbl>,
## #   Zhejiang_Enterprise <int>, Zhejiang_Output <dbl>,
## #   Anhui_Enterprise <int>, Anhui_Output <dbl>, Fujian_Enterprise <int>,
## #   Fujian_Output <dbl>, Jiangxi_Enterprise <int>, Jiangxi_Output <dbl>,
## #   Shandong_Enterprise <int>, Shandong_Output <dbl>,
## #   Henan_Enterprise <int>, Henan_Output <dbl>, Hubei_Enterprise <int>,
## #   Hubei_Output <dbl>, Hunan_Enterprises <int>, Hunan_Output <dbl>,
## #   Guangdong_Enterprise <int>, Guangdong_Output <dbl>,
## #   Guangxi_Enterprise <int>, Guangxi_Output <dbl>,
## #   Hainan_Enterprise <int>, Hainan_Output <dbl>,
## #   Chongqing_Enterprise <int>, Chongqing_Output <dbl>,
## #   Sichuan_Enterprise <int>, Sichuan_Output <dbl>,
## #   Guizhou_Enterprise <int>, Guizhou_Output <dbl>,
## #   Yunnan_Enterprise <int>, Yunnan_Output <dbl>, Tibet_Enterprise <int>,
## #   Tibet_Output <dbl>, Shaanxi_Enterprise <int>, Shaanxi_Output <dbl>,
## #   Gansu_Enterprise <int>, Gansu_Output <dbl>, Qinghai_Enterprise <int>,
## #   Qinghai_Output <dbl>, Ningxia_Enterprise <int>, Ningxia_Output <dbl>,
## #   Xinjiang_Enterprise <int>, Xinjiang_Output <dbl>
year2k = filter(rearrange, Year >= 2000)
shang.bei = select(year2k, Year, Shanghai_Enterprise, Shanghai_Output, Beijing_Enterprise, Beijing_Output)
shang.bei
## # A tibble: 18 x 5
##     Year Shanghai_Enterpri~ Shanghai_Output Beijing_Enterpr~ Beijing_Output
##    <int>              <int>           <dbl>            <int>          <dbl>
##  1  2017               8122          36094.             3231            NA 
##  2  2016               8351          31136.             3340         18087.
##  3  2015               8994          31050.             3548         17450.
##  4  2014               9469          32665.             3686         18453.
##  5  2013               9796          32089.             3641         17371.
##  6  2012               9772          31548.             3692         15596.
##  7  2011               9962          32445.             3746         14514.
##  8  2010              16684          30114.             6884         13700.
##  9  2009              17906          24091.             6890         11039.
## 10  2008              18792          25121.             7205         10413.
## 11  2007              15099          22260.             6397          9648.
## 12  2006              14404          18573.             6400          8210 
## 13  2005              14809          15768.             6300          6946.
## 14  2004              15766          12885.             6871          4881.
## 15  2003              11098          10343.             4019          3810.
## 16  2002              10057           7741.             4551          3173.
## 17  2001               9762           7004.             4356          2909.
## 18  2000               8574           6205.             4572          2565.
finaltib = mutate(shang.bei, Output_Ratio = Beijing_Output / Shanghai_Output)
finaltib
## # A tibble: 18 x 6
##     Year Shanghai_Enterp~ Shanghai_Output Beijing_Enterpr~ Beijing_Output
##    <int>            <int>           <dbl>            <int>          <dbl>
##  1  2017             8122          36094.             3231            NA 
##  2  2016             8351          31136.             3340         18087.
##  3  2015             8994          31050.             3548         17450.
##  4  2014             9469          32665.             3686         18453.
##  5  2013             9796          32089.             3641         17371.
##  6  2012             9772          31548.             3692         15596.
##  7  2011             9962          32445.             3746         14514.
##  8  2010            16684          30114.             6884         13700.
##  9  2009            17906          24091.             6890         11039.
## 10  2008            18792          25121.             7205         10413.
## 11  2007            15099          22260.             6397          9648.
## 12  2006            14404          18573.             6400          8210 
## 13  2005            14809          15768.             6300          6946.
## 14  2004            15766          12885.             6871          4881.
## 15  2003            11098          10343.             4019          3810.
## 16  2002            10057           7741.             4551          3173.
## 17  2001             9762           7004.             4356          2909.
## 18  2000             8574           6205.             4572          2565.
## # ... with 1 more variable: Output_Ratio <dbl>